Affiliation:
1. Athabasca University, Canada
Abstract
The search for causal relations from observational data is an open problem that spans many fields. In the area of learning, this is especially important. The ability to determine the effect of a new teaching strategy or the cause of an upswing in student performance is always desirable. In computer science, integrated development environments (IDE) offer students many features promising to instill the necessary competency skills for migration to industry. In this chapter, current causal discovery methods are applied to investigate a causal link between IDE consultations and student competency which is measured by the number of issues at the end of the coding timeline. The coding activities of students were timestamped over the coding lifetime. Due to the nature of the data, the authors were able to test for causality using methods for static and methods for dynamic data. The authors show the presence of a causal link between IDE consults and student improvement. In addition, they show the time it takes to see the effect of a system consult.
Reference18 articles.
1. Behrendt, S., Dimpfl, T., Peter, F. J., & Zimmermann, D. J. (2018). RTransferEntropy: Measuring Information Flow Between Time Series with Shannon and Renyi Transfer Entropy. In R Package Version 0.2. 7.
2. Assessing Learning Analytics Impact on Coding Competence Growth
3. Investigating Causal Relations by Econometric Models and Cross-spectral Methods
4. Time Series Analysis, Cointegration, and Applications
5. Nonlinear causal discovery with additive noise models.;P.Hoyer;Advances in Neural Information Processing Systems,2008